i am here

i want to merge all rowsof 3 rd col so that output looks like...
end result should be....

The screenshots show a desire to concatenate every value from the 3rd CSV column into one string (third column = index 2 with 0-based indexing). 's suggestion to try pandas is a good starting point because it handles quoting, delimiters and encodings reliably. Below are two practical approaches: a concise pandas solution and a memory-friendly pure-Python (csv) alternative.
Pandas (simple):
import pandas as pd
df = pd.read_csv('input.csv') # set sep='\t' or other if not comma
col = df.iloc[:, 2].dropna().astype(str)
merged = col.str.cat(sep=' ') # change sep to ',' or '\n' if desired
print(merged) Pandas (large files, streaming):
parts = []
for chunk in pd.read_csv('input.csv', usecols=[2], header=None, chunksize=100000):
parts.append(chunk.iloc[:,0].dropna().astype(str).str.cat(sep=' '))
merged = ' '.join(parts) Pure-Python (csv, streaming to avoid high memory):
import csv
with open('input.csv', newline='', encoding='utf-8') as f, open('merged.txt', 'w', encoding='utf-8') as out:
reader = csv.reader(f)
# skip header if present: next(reader)
first = True
for row in reader:
if len(row) >= 3 and row[2].strip():
if not first:
out.write(' ')
out.write(row[2])
first = False Notes and troubleshooting: confirm whether the file has a header (skip it), confirm the delimiter (comma vs tab), and handle encoding (utf-8 vs others). If fields contain commas or embedded newlines, rely on pandas or the csv module rather than manual splitting. For deduplication while preserving order, use an OrderedDict or a simple seen set with list append. These snippets complement 's point and give both the quick pandas route and a low-memory fallback for very large CSVs.
Jump to Post— vegaseat 1,735You might want to look at third party module pandas (it is a free download)
http://pandas.pydata.org/pandas-docs/stable/tutorials.html#pandas-cookbook
its not solved yet please help
You might want to look at third party module pandas (it is a free download)
http://pandas.pydata.org/pandas-docs/stable/tutorials.html#pandas-cookbook
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